Contact details +6469517112

Dr Yi Wang PhD

Senior Lecturer

School of Mathematical and Computational Sciences

Dr. Yi Wang is a Senior Lecturer in Computer Science at Massey University, New Zealand, specializing in computational neuroscience, machine learning, and neural decoding. He leads interdisciplinary research combining computational models and electrophysiological data to investigate brain mechanisms underlying memory and emotion. Yi's work has appeared in leading journals such as Nature Communications and Hippocampus. He has secured competitive funding, including an early career grant from the Japan Society for the Promotion of Science (JSPS), and actively supervises students in neural decoding and brain-computer interface projects.

Professional

Contact details

  • Ph: +64 6 951 7112
    Location: B3.26, Science Tower B
    Campus: Manawatu

Qualifications

  • Doctor of Philosophy - University of Otago (2020)

Prizes and Awards

  • Wiley Top Cited Article 2022-2023 - Wiley (2022)

Research Expertise

Research Interests

  • Application of machine learning and deep learning techniques for analyzing complex neural data such as electrophysiological signals (EEG, LFP, spike trains).
  • Neural decoding methods and development of brain-computer interfaces (BCI) for real-time prediction and interpretation of neural activity related to behavior and cognition.
  • Investigating multi-regional brain interactions and network dynamics involved in memory age and fear conditioning using interpretable AI approaches.
  • Design and implementation of interpretable and explainable AI models to reveal neural circuit functions and biomarker identification.
  • Neuroinformatics approaches integrating multi-modal brain data to advance data-driven understanding of neural computation and brain disorders.

Thematics

Health and Well-being

Area of Expertise

Field of research codes
Artificial Intelligence and Image Processing (080100):
Biochemistry and Cell Biology (060100): Bioinformatics (060102): Biological Sciences (060000): Cell Metabolism (060104):
Cognitive Sciences (170200): Computer Perception, Memory and Attention (170201):
Computer-Human Interaction (080602): Information And Computing Sciences (080000): Information Systems (080600):
Knowledge Representation and Machine Learning (170203): Neurocognitive Patterns and Neural Networks (170205):
Pattern Recognition and Data Mining (080109):
Psychology And Cognitive Sciences (170000)

Keywords

Machine Learning, Deep Learning, Signal Processing, Computational Neuroscience, Electrophysiological Signal Analysis, Brain Computer Interface

Research Outputs

Journal

Dai, F., Hossain, MA., & Wang, Y. (2025). State of the Art in Parallel and Distributed Systems: Emerging Trends and Challenges. Electronics Switzerland. 14(4)
[Journal article]Authored by: Wang, Y.
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10
Wang, X., Liesaputra, V., Liu, Z., Wang, Y., & Huang, Z. (2024). An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification. Artificial Intelligence in Medicine. 147(January 2024), Retrieved from https://www.sciencedirect.com/science/article/pii/S093336572300252X
[Journal article]Authored by: Wang, Y.
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Makino, Y., Wang, Y., & McHugh, TJ. (2024). Multi-regional control of amygdalar dynamics reliably reflects fear memory age. Nature Communications. 15(1)
[Journal article]Authored by: Wang, Y.
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He, H., Wang, Y., & McHugh, TJ. (2023). Behavioral status modulates CA2 influence on hippocampal network dynamics. Hippocampus. 33(3), 252-265
[Journal article]Authored by: Wang, Y.
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5

Thesis

Wang, Y. (2020). EEG-based Anxious Personality Prediction, a thesis submitted for the degree of Doctor of Philosophy at the University of Otago, Dunedin, New Zealand. (Doctoral Thesis, University of Otago, Dunedin, New Zealand) Wang, Y. (2020). EEG-based Anxious Personality Prediction, a thesis submitted for the degree of Doctor of Philosophy at the University of Otago, Dunedin, New Zealand. (Doctoral Thesis)
[Doctoral Thesis]Authored by: Wang, Y.
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Conference

Wang, Y., McCane, B., McNaughton, N., Huang, Z., Shadli, H., & Neo, P. (2019). AnxietyDecoder: An EEG-based Anxiety Predictor using a 3-D Convolutional Neural Network. Proceedings of the International Joint Conference on Neural Networks. Vol. 2019-July
[Conference Paper in Published Proceedings]Authored by: Wang, Y.
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12
Wang, Y., Huang, Z., McCane, B., & Neo, P. (2018). EmotioNet: A 3-D Convolutional Neural Network for EEG-based Emotion Recognition. Proceedings of the International Joint Conference on Neural Networks. Vol. 2018-July
[Conference Paper in Published Proceedings]Authored by: Wang, Y.
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91
Wang, Y., Zhao, J., Fu, W., & Zhang, H.Hardware design of large-scale sensor-based Electrical Capacitance Tomography systems. Proceedings 2015 Chinese Automation Congress Cac 2015. (pp. 2048 - 2051).
[Conference]Authored by: Wang, Y.
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2

Consultancy and Languages

Consultancy

  • Feb. 2024 to Present - Institute of Physical and Chemical Research (RIKEN), Japan
    Visiting Scientist

Languages

  • English
    Last used: Currently
    Spoken ability: Excellent
    Written ability: Excellent
  • Chinese
    Last used: Currently
    Spoken ability: Excellent
    Written ability: Excellent
  • Japanese
    Last used: 2023
    Spoken ability: Needs work
    Written ability: Needs work

Teaching and Supervision

Teaching Statement

I have extensive experience delivering high-quality lectures and developing course materials in computer science subjects, including Programming, Algorithms, Data Structures, Machine Learning, and Computational Neuroscience. I actively engage students through interactive teaching methods, continuous assessment, and timely feedback to enhance learning outcomes. Additionally, I support students' academic growth by providing individual guidance and fostering a collaborative learning environment. My teaching approach emphasizes clarity, practical application, and integration of current research to inspire and motivate students.

Most importantly, I hope students enjoy and benefit from my teaching ;)

Contact Supervisor

Graduate Supervision Statement

My style of graduate supervision is collaborative and student-centered, emphasizing clear communication, regular progress discussions, and fostering independent critical thinking. I support students in developing strong technical skills in computational neuroscience, machine learning, and neural data analysis while encouraging creativity and problem-solving. I tailor my guidance to individual needs, promoting both theoretical understanding and practical implementation. With my support and supervision, I aim to help students enjoy and thrive throughout their academic journey.


Media and Links

Media

  • 09 Dec 2024 - Online
    Neural Activity Reflecting Memory Formation Timing
    https://www.riken.jp/press/2024/20241209_2/index.html

Other Links